DiscoPoP: A Profiling Tool to Identify Parallelization Opportunities

Zhen Li (), Rohit Atre (), Zia Ul-Huda (), Ali Jannesari () and Felix Wolf ()
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Zhen Li: German Research School for Simulation Sciences
Rohit Atre: Aachen Institute for Advanced Study in Computational Engineering Science
Zia Ul-Huda: German Research School for Simulation Sciences
Ali Jannesari: German Research School for Simulation Sciences
Felix Wolf: German Research School for Simulation Sciences

A chapter in Tools for High Performance Computing 2014, 2015, pp 37-54 from Springer

Abstract: Abstract The stagnation of single-core performance leaves application developers with software parallelism as the only option to further benefit from Moore’s Law. However, in view of the complexity of writing parallel programs, the parallelization of myriads of sequential legacy programs presents a serious economic challenge. A key task in this process is the identification of suitable parallelization targets in the source code. We have developed a tool called DiscoPoP showing how dependency profiling can be used to automatically identify potential parallelism in sequential programs. Our method is based on the notion of computational units, which are small sections of code following a read-compute-write pattern that can form the atoms of concurrent scheduling. DiscoPoP covers both loop and task parallelism. Experimental results show that reasonable speedups can be achieved by parallelizing sequential programs manually according to our findings. By comparing our findings to known parallel implementations of sequential programs, we demonstrate that we are able to detect the most important code locations to be parallelized.

Keywords: Parallel Implementation; Parallel Task; Computational Unit; Benchmark Suite; Pattern Vector (search for similar items in EconPapers)
Date: 2015
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-16012-2_3

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DOI: 10.1007/978-3-319-16012-2_3

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